General Info

Teachers

• Inge Li Gørtz, inge@dtu.dk, office hours Fridays, 12.15-12.45 on Zoom id: 681 9419 7720
• Philip Bille, phbi@dtu.dk, office hours Fridays 12.30-13.00 on Zoom id: 677 3898 7472

Teaching Assistants

Prerequisites Undergraduate level courses in algorithms and data structures (comparable to 02105 + 02110) and mathematical maturity. You should have a working knowledge of algorithm analysis (e.g. asymptotic notation, worst case analysis, amortized analysis, basic analysis of randomized algorithms), data structures (e.g. stacks, queues, linked lists, trees, heaps, priority queues, hash tables, balanced binary search trees, tries), graph algorithms (e.g. BFS, DFS, single source shortest paths, minimum spanning trees, topological sorting), dynamic programming, divide-and-conquer, and NP-completeness (e.g. basic reductions).

Weekplan

The weekplan is preliminary It will be updated during the course. Under each week there is a number of suggestions for reading material regarding that weeks lecture. It is not the intention that you read ALL of the papers. It is a list of papers and notes where you can read about the subject discussed at the lecture.

Hand-in Exercises

The course features number of non-mandatory/voluntary hand-in exercises that are posted throughout the course. These are handed in as follows.

Use the template.tex file to prepare your write up your solution to the exercises. Do not repeat the problem statement in your solutions and do not modify the template. Compile your solutions using LaTeX. The maximum size of the finished pdf must be at most 3 pages. To submit your solution:

Mandatory Exercise

The course features a few mandatory exercises during the course. The exercises are handed-in according to the same guidelines as the non-mandatory/voluntary hand-in exercises, except that the page limit may be different. The collaboration policy is also different.

Collaboration policy for the mandatory exercise

• You may collaborate in groups of up to 3 students on the hand in exercises. The collaborators must be listed in your solution (see template).
• Collaboration is limited to discussion of ideas only, and you should write up the solutions entirely on your own.
• Do not use or seek out solutions from previous years of the course, solutions from similar courses, solutions found on the internet, or solutions obtained with the help of AI tools.

Violation of the collaboration policy is strictly prohibited.

Frequently Asked Questions

How should I write my exercises? The ideal writing format for exercises is classical scientific writing, such as the writing found in the peer-reviewed articles listed as reading material for this course (not textbooks and other pedagogical material). One of the objectives of this course is to practice and learn this kind of writing. A few tips:

• Write things directly: Cut to the chase and avoid anything that is not essential. Test your own writing by answering the following question: “Is this the shortest, clearest, and most direct exposition of my ideas/analysis/etc.?”
• Add structure: Don’t mix up description and analysis unless you know exactly what you are doing. For a data structure explain following things separately: The contents of the data structure, how to build it, how to query/update it, correctness, analysis of space, analysis of query/update time, and analysis of preprocessing time. For an algorithm explain separately what it does, correctness, analysis of time complexity, and analysis of space complexity.
• Be concise: Convoluted explanations, excessively long sentences, fancy wording, etc. have no in place scientific writing. Do not repeat the problem statement.
• Try to avoid pseudocode: Generally, aim for human readable description of algorithms that can easily and unambiguously be translated into code.
• Examples for support: Use figures and examples to illustrate key points of your algorithms and data structures.

How much do the mandatory exercise count in the final grade? The final grade is an overall evaluation of your mandatory exercise and the oral exam combined. Thus, there is no precise division of these part in the final grade. However, expect that (in most cases, and under normal circumstances) the mandatory exercises account for a non-trivial fraction of the final grade.

What do I do if I want to do a MSc/BSc thesis or project in Algorithms? Great! Algorithms is an excellent topic to work on :-) and Algorithms for Massive Data Sets is designed to prepare you to write a strong thesis. Some basic tips and points.

• Let us know well in advance: Identifying an interesting problem in algorithms that matches your interest can take time. With enough time to go over the related litterature and study up on relevant topics your project will likely be more succesful. It may also be a good idea to do an initial “warm up” project before a large thesis to test ideas or survey an area.
• Join the community: It is very good idea to enter the local algorithms community at DTU and the Copenhagen area to get a feel for what kind of stuff you could work on for your thesis and what thesis work algorithms is about. Talk to other students doing thesis work in algorithms. Go to algorithms talks and thesis defenses in algorithms.
• Collaborate: We strongly encourage you to do your thesis in pairs. We think that having a collaborator to discuss with greatly helps in many aspects of thesis work in algorithms. Our experience confirms this.
• No strings attached. Choosing a topic for your thesis is important. You are welcome to discuss master thesis topics with us without pressure to actually write your thesis in algorithms. We encourage you to carefully select your topic.